基于CST参数化的翼型优化遗传算法研究

用外形建模的CST参数化方法,构建翼型几何可直接利用其后缘角、前缘半径等几何特征,其控制参数更能反映翼型特有的气动敏感性,有助于遗传算法搜索寻优。基于这一参数化方法,结合遗传算法,构造了用于翼型优化的设计方法。算法中,CST控制参数作为设计变量,采用二进制编码,并通过引入精英策略,提高了遗传算法的收敛性能。算法适应度评估涉及的流场求解则采用了基于Jameson有限体积法的Euler方程解算程序。先以NACA0012翼型为例,以其某一已知的表面压力分布为目标,进行了遗传算法的重构运算,给出了重构的翼型几何外形,验证了方法。在此基础上,进行了带约束的跨音速翼型优化设计,给出了升力系数极大化和阻力系数极小化等设计算例,展示出翼型优化设计的效果。

Research on Genetic Algorithm for Aerodynamic Shape Optimization Based on CST

The CST parameterization method is applied to the aerodynamic shape optimization of airfoils.A binary coded Genetic Algorithm(GA) is used as the fundamental optimization method.Elitist strategy is introduced into GA to improve its performance of convergence.A finite volume Euler solver based on Jameson method is used for aerodynamic calculation.The presented results of airfoil reconstruction show that the developed method is feasible.The method is then applied to the problems including lift coefficient maximization and drag coefficient minimization under prescribed constraints,to validate its performance when used in aerodynamic shape optimization of airfoils.